[2602.00423] Federated-inspired Single-cell Batch Integration in Latent Space

Computer Science > Machine Learning arXiv:2602.00423 (cs) [Submitted on 31 Jan 2026 (v1), last revised 27 Feb 2026 (this version, v2)] Title:Federated-inspired Single-cell Batch Integration in Latent Space Authors:Quang-Huy Nguyen, Zongliang Yue, Hao Chen, Wei-Shinn Ku, Jiaqi Wang View a PDF of the paper titled Federated-inspired Single-cell Batch Integration in Latent Space, by Quang-Huy Nguyen and Zongliang Yue and Hao Chen and Wei-Shinn Ku and Jiaqi Wang View PDF HTML (experimental) Abstract:Advances in single-cell RNA sequencing enable the rapid generation of massive, high-dimensional datasets, yet the accumulation of data across experiments introduces batch effects that obscure true biological signals. Existing batch correction approaches either insufficiently correct batch effects or require centralized retraining on the complete dataset, limiting their applicability in distributed and continually evolving single-cell data settings. We introduce scBatchProx, a post-hoc optimization method inspired by federated learning principles for refining cell-level embeddings produced by arbitrary upstream methods. Treating each batch as a client, scBatchProx learns batch-conditioned adapters under proximal regularization, correcting batch structure directly in latent space without requiring raw expression data or centralized optimization. The method is lightweight and deployable, optimizing batch-specific adapter parameters only. Extensive experiments show that scBatchProx cons...